# In [18]: a=torch.linspace(-100,100,10)

# In [19]: a
# Out[19]: 
# tensor([-100.0000,  -77.7778,  -55.5556,  -33.3333,  -11.1111,   11.1111,
#           33.3333,   55.5555,   77.7778,  100.0000])

# In [20]: torch.sigmoid(a)
# Out[20]: 
# tensor([0.0000e+00, 1.6655e-34, 7.4564e-25, 3.3382e-15, 1.4945e-05, 9.9999e-01,
#         1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00])

# In [21]: from torch.nn import functional as F

# In [22]: F.sigmoid(a)
# Out[22]: 
# tensor([0.0000e+00, 1.6655e-34, 7.4564e-25, 3.3382e-15, 1.4945e-05, 9.9999e-01,
#         1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00])

# In [23]: a=torch.linspace(-1,1,10)

# In [24]: torch.tanh(a)
# Out[24]: 
# tensor([-0.7616, -0.6514, -0.5047, -0.3215, -0.1107,  0.1107,  0.3215,  0.5047,
#          0.6514,  0.7616])


# In [25]: a=torch.linspace(-1,1,10)

# In [26]: torch.relu(a)
# Out[26]: 
# tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1111, 0.3333, 0.5556, 0.7778,
#         1.0000])

# In [28]: F.relu(a)
# Out[28]: 
# tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1111, 0.3333, 0.5556, 0.7778,
#         1.0000])